Culture shapes whether the pursuit of happiness predicts higher or lower well-being.
Bibliographic record
Abstract
Pursuing happiness can paradoxically impair well-being. Here, the authors propose the potential downsides to pursuing happiness may be specific to individualistic cultures. In collectivistic (vs. individualistic) cultures, pursuing happiness may be more successful because happiness is viewed--and thus pursued--in relatively socially engaged ways. In 4 geographical regions that vary in level of collectivism (United States, Germany, Russia, East Asia), we assessed participants' well-being, motivation to pursue happiness, and to what extent they pursued happiness in socially engaged ways. Motivation to pursue happiness predicted lower well-being in the United States, did not predict well-being in Germany, and predicted higher well-being in Russia and in East Asia. These cultural differences in the link between motivation to pursue happiness and well-being were explained by cultural differences in the socially engaged pursuit of happiness. These findings suggest that culture shapes whether the pursuit of happiness is linked with better or worse well-being, perhaps via how people pursue happiness.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".